Shmueli, G., P. C. Bruce, I. Yahav, N. R. Patel, and K. C. Lichtendahl, Jr., Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, 1, New Jersey, John Wiley & Sons, 2017.

Shmueli, G., and H. Li, "Practical Time Series Forecasting: A Hands-On Guide [Chinese Edition] (时间序列预测实践教程)", Practical Analytics: Tsinghua University Press, 2012.

Shmueli, G., and K. C. Lichtendahl, Jr., "Practical Time Series Forecasting with R: A Hands-On Guide", Practical Analytics: Axelrod Schnall, pp. 208, 2015.

Shmueli, G., and K. C. Lichtendahl, Jr., "Practical Time Series Forecasting with R: A Hands-On Guide [2nd Edition]", Practical Analytics, 2: Axelrod Schnall, 2016.

Shmueli, G., and K. C. Lichtendahl, Jr., "Practical Time Series Forecasting with R: A Hands-On Guide [Indian Edition]", Practical Analytics: Axelrod Schnall, pp. 208, 2016.

Yahav, I., T. Lotze, and G. Shmueli, "Algorithm Combination for Improved Detection in Biosurveillance", Infectious Disease Informatics and Biosurveillance: Integrated Series in Information Systems, 1, vol. 27: Springer, pp. 173-189, 2011. 143493_1_En_8_Chapter_OnlinePDF.pdf (1.55 MB)

Yahav, I., T. Lotze, and G. Shmueli, "Algorithm Combination for Improved Detection in Biosurveillance", Infectious Disease Informatics and Biosurveillance: Integrated Series in Information Systems, 1, vol. 27: Springer, pp. 173-189, 2011. 143493_1_En_8_Chapter_OnlinePDF.pdf (1.55 MB)

Yahav, I., and G. Shmueli, "Algorithm Combination for Improved Performance in Biosurveillance Systems", Intelligence and Security Informatics: Biosurveillance, vol. 4506, pp. 91-102, 2007.

Yahav, I., and G. Shmueli, "Algorithm Combination for Improved Performance in Biosurveillance Systems", Intelligence and Security Informatics: Biosurveillance, vol. 4506, pp. 91-102, 2007.

Lotze, T., G. Shmueli, and I. Yahav, "Simulating and Evaluating Biosurveillance Datasets", Biosurveillance: Methods and Case Studies: Chapman and Hall, 2010. Book Chapter (1.93 MB)

Lotze, T., and G. Shmueli, "Ensemble Forecasting for Disease Outbreak Detection", 23rd AAAI Conference on Artificial Intelligence (AAAI-08), Chicago, IL, 2008. AAAI7LotzeT.pdf (61 KB)

Yahav, I., and G. Shmueli, "Predicting Potential Survival Rates of Kidney Transplant Candidates From Databases With Existing Allocation Policies", 5th INFORMS Workshop on Data Mining and Healh Informatics, Austin, TX, 05/11/2010. INFORMS 2010 DMHI.pdf (123.33 KB)

Lotze, T., and G. Shmueli, "On the relationship between forecast accuracy and detection performance: An application to biosurveillance", 2008 IEEE Conference on Technologies for Homeland Security, Boston, MA, 2008. ieee.pdf (243.68 KB)

Lotze, T., G. Shmueli, S. Murphy, and H. S. Burkom, "A Wavelet-based Anomaly Detector for Early Detection of Disease Outbreaks", Proceedings of the 23rd International Conference on Machine Learning (ICML), Workshop on Machine Learning Algorithms for Surveillance and Event Detection, Pittsburgh, PA, 2006.

Ashouri, M., K. Cai, FR. Lin, and G. Shmueli, "Assessing the Value of an Information System for Developing Predictive Analytics: The Case of Forecasting School-Level Demand in Taiwan", Service Science, vol. 10, pp. 58–75, 2018.

Lotze, T., and G. Shmueli, "How does improved forecasting benefit detection? An application to biosurveillance", International Journal on Forecasting, vol. 25, pp. 467-483, 2009. IJF2009 ForecastingBiosurveillance.pdf (2.19 MB)

Lotze, T., S. Murphy, and G. Shmueli, "Preparing Biosurveillance Data for Classic Monitoring", Advances in Disease Surveillance, vol. 6, 2008. DataPreparation AdvDisSurv.pdf (470.11 KB)

Lin, M., H. C. Lucas, and G. Shmueli, "Too Big To Fail: Large Samples and the P-Value Problem", Information Systems Research, vol. 24, issue 4, pp. 906-917, 2013. Article (269.29 KB)

Lin, M., H. C. Lucas, and G. Shmueli, "Too Big To Fail: Large Samples and the P-Value Problem", Information Systems Research, vol. 24, issue 4, pp. 906-917, 2013. Article (269.29 KB)

Shmueli, G., T. Lotze, and I. Yahav, "Simulating Multivariate Syndromic Time Series and Outbreak Signatures", Working Paper RHS-06-054: Smith School of Business, University of Maryland, 2007.